Abstract
ABSTRACTIn this paper, we propose a correction of the Mobility Entropy indicator (ME) used to describe the diversity of individual movement patterns as can be captured by data from mobile phones. We argue that a correction is necessary because standard calculations of ME show a structural dependency on the geographical density of observation points, rendering results biased and comparisons between regions incorrect. As a solution, we propose the Corrected Mobility Entropy (CME). We apply our solution to a French mobile phone dataset with ∼18.5 million users. Results show CME to be less correlated to cell-tower density (r = –0.17 instead of –0.59 for ME). As a spatial pattern of mobility diversity, we find CME values to be higher in suburban regions compared to their related urban centers, while both decrease considerably with lowering urban center sizes. Based on regression models, we find mobility diversity to relate to factors like income and employment. Additionally, using CME reveals the role of car use in relation to land use, which was not recognized when using ME values. Our solution enables a better description of individual mobility at a large scale, which has applications in official statistics, urban planning and policy, and mobility research.
Highlights
The study of large-scale human movement has benefitted greatly from advances in information technologies, allowing for the collection of data on individual movement for largescale populations
We focused on the derivation of the so-called Mobility Entropy (ME) from mobile phone records as an indicator of the diversity of individual movement
Being the first to look into nationwide, spatial patterns of mobility entropy, we raised the issue that standard ME calculations depict a structural dependency on cell-tower density, rendering comparison of ME values between regions biased
Summary
The study of large-scale human movement has benefitted greatly from advances in information technologies, allowing for the collection of data on individual movement for largescale populations. Traditional approaches, such as interviews, questionnaires, and travel diaries are ill-suited to study large populations because they require considerable effort from both researchers and participants, which in turn results in datasets with smaller sample sizes, limited observation periods, and even incomplete information (Chen et al, 2014; Janzen et al, 2016; Janzen et al, 2018). One downside of datasets with large samples of individuals is that their investigation tends to presume universality of the observed empirical properties These works are, either implicitly or explicitly, advocating a disputable universality of human movement leading to a generalist and limited view of human mobility (Schwanen, 2016)
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